--- base_model: facebook/dino-vitb16 library_name: transformers pipeline_tag: image-classification tags: - probex - model-j - weight-space-learning --- # Model-J: DINO Model (model_idx_0174) This model is part of the **Model-J** dataset, introduced in: **Learning on Model Weights using Tree Experts** (CVPR 2025) by Eliahu Horwitz*, Bar Cavia*, Jonathan Kahana*, Yedid Hoshen

🌐 Project | 📃 Paper | 💻 GitHub | 🤗 Dataset

![ProbeX](https://raw.githubusercontent.com/eliahuhorwitz/ProbeX/main/imgs/poster.png) ## Model Details | Attribute | Value | |---|---| | **Subset** | DINO | | **Split** | train | | **Base Model** | `facebook/dino-vitb16` | | **Dataset** | CIFAR100 (50 classes) | ## Training Hyperparameters | Parameter | Value | |---|---| | Learning Rate | 0.0001 | | LR Scheduler | constant_with_warmup | | Epochs | 6 | | Max Train Steps | 1998 | | Batch Size | 64 | | Weight Decay | 0.03 | | Seed | 174 | | Random Crop | True | | Random Flip | True | ## Performance | Metric | Value | |---|---| | Train Accuracy | 0.9344 | | Val Accuracy | 0.8352 | | Test Accuracy | 0.8314 | ## Training Categories The model was fine-tuned on the following 50 CIFAR100 classes: `cockroach`, `dolphin`, `boy`, `pine_tree`, `bus`, `can`, `snake`, `orchid`, `lamp`, `apple`, `bed`, `lobster`, `tiger`, `seal`, `clock`, `plate`, `squirrel`, `cloud`, `chimpanzee`, `plain`, `sea`, `otter`, `raccoon`, `keyboard`, `flatfish`, `whale`, `pear`, `shrew`, `telephone`, `turtle`, `caterpillar`, `cup`, `kangaroo`, `beaver`, `bicycle`, `chair`, `possum`, `bear`, `porcupine`, `ray`, `trout`, `table`, `worm`, `willow_tree`, `man`, `maple_tree`, `motorcycle`, `wardrobe`, `lizard`, `mountain`